Can LLMs Infer Conversational Agent Users' Personality Traits from Chat History?
Sensitive information, such as knowledge about an individual's personality, can be can be misused to influence behavior (e.g., via personalized messaging). To assess to what extent an individual's personality can be inferred from user interactions with LLM-based conversational agents (CAs), we analyze and quantify related privacy risks of using CAs. We collected actual ChatGPT logs from N=668 participants, containing 62,090 individual chats, and report statistics about the different types of shared data and use cases. We fine-tuned RoBERTa-base text classification models to infer personality traits from CA interactions. The findings show that these models achieve trait inference with accu...
| Authors | Derya Cögendez, Verena Zimmermann, Noé Zufferey |
|---|---|
| Primary category | cs.CL |
| Published | 31 March 2026 |
| Last updated at source | 4 May 2026 |
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